Prediction of groundwater level fluctuations under climate change based on machine learning algorithms in the Mashhad Aquifer, Iran

Due to population growth in recent years and climate change in arid and semi-arid regions, the lack of rainfall and the reduction of surface water flows required in various sectors, monitoring, and projection of the climate change impact on the groundwater level (GWL) in the future are vital in the management and control of these resources. The purpose of this study is the projection of climate change's impact on the GWL fluctuations in the Mashhad aquifer during the future period (2022-2064). In the first step, the climatic variables using the ACCESS-CM2 model under the Shared Socio-economic Pathway (SSP) 5-8.5 scenario were extracted. In the second step, different machine learning algor-ithms were used to predict the GWL fluctuations under climate change in the future. Our results point out that temperatures and evaporation will increase in the autumn season and precipitation will decrease by 26% in the future in the Mashhad aquifer. The results showed that the Radial Basis Function Neural Network (RBFNN) model had an excellent performance in predicting the GWL compared to other models. Based on the result of the RBF model, the GWL will decrease by 6.60 m under the SSP5-8.5 scenario in the future.